<?xml version="1.0" encoding="utf-8"?>
<?xml-stylesheet href="client.xsl" type="text/xsl"?>
<article article-type="other">
<front>
<journal-meta>
<journal-id/>
<issn/>
<banner>
<!--<href>banner.jpg</href>-->
<size width="100%"/>
</banner>
</journal-meta>
<article-meta>
<title-group>
<article-title>F-Trade: An Agent-Mining Symbiont for Financial Services</article-title>
</title-group>

<author><a href="mailto:lbcao@it.uts.edu.au"><name>Longbing Cao</name></a></author>
<aff>Faculty of Information Technology,  University of Technology Sydney, Australia</aff>

<author><a href="mailto:chengqi@it.uts.edu.au"><name>Chengqi Zhang</name></a></author>
<aff>Faculty of Information Technology,  University of Technology Sydney, Australia</aff>


</article-meta></front>
<body>
<abstract>
<title>ABSTRACT</title>
<p>The interaction and integration of agent technology and data mining presents prominent benefits to solve some of challenging issues in individual areas. For instance, data mining can enhance agent earning, while agent can benefit data mining with distributed pattern discovery. In this paper, we summarize the main functionalities and features of an agent service and data mining symbiont -- F-Trade. The F-Trade is constructed in Java agent service following the theory of open complex agent systems.We demonstrate the roles of agents in building up the F-Trade, as well as how agents can support data mining. On the other hand,data mining is used to strengthen agents. F-Trade provides
flexible and efficient services of trading evidence back-testing,optimization and discovery, as well as plug and play of algorithms,data and system modules for financial trading and surveillance
with online connectivity to huge quantities of global market data. </p>
</abstract>
<fpdf>
<href>pdflogo.jpg</href>
<hpdf>Cao-F-Trade</hpdf>
</fpdf>
</body>
</article>
